Teacher-Student Curriculum Learning (TSCL) [1]} is a training scheme where an RL-trained teacher samples environments in which student agents are trained. The teacher is rewarded for generating environments where student can learn most according to some measure of student behavior. Domain randomization, where the environment distribution is not adapted during training, can be considered a special case of TSCL: If the teacher is trained using maximum-entropy RL with constant teacher reward the teacher policy will converge to a stationary distribution.
